Interactive Maps

1 Map 1: Randomly Selected Gas Stations

1.1 Overview

Our first visualization will be an interactive map, containing various locations of gas stations from the contential United States. Each gas station will be a point on the map, and each point will have a hover text box containing state, county, address, and zip code.

1.2 Randomly Selecting Data

Since our source data contains nearly 73,000 observations, we will randomly select 500 observations.

gas<-read.csv("https://pengdsci.github.io/datasets/POC/POC.csv")

rand.df <- gas[sample(nrow(gas), size=500), ]

1.3 Mapping the Data

We will use the plotly package to map the data onto an interacitve map.

g <- list(      scope = 'p',
                projection = list(type = 'albers usa'),
                showland = TRUE,
                landcolor = toRGB("gray95"),
                subunitcolor = toRGB("gray85"),
                countrycolor = toRGB("gray85"),
                countrywidth = 0.5,
                subunitwidth = 0.5
)


fig <- plot_geo(rand.df, lat = ~ycoord, lon = ~xcoord) %>% 
  add_markers( text = ~paste(STATE, county, ADDRESS, ZIPnew,
                             
                             sep = "<br>"),
               #color = , 
               symbol = "circle", 
               #size = , 
               hoverinfo = "text")   %>% 
  layout( title = 'Randomly Selected US Gas Stations', 
          geo = g )

fig

The above is a demonstration of a simple - yet effective - way to visualize data on a map. Our next will be a little more complex.

2 Map 2: Crime in Philadelphia, 2023

2.1 Overview

For this visualization, we will begin with a dataset of crimes committed in the great city of Philadelphia between 2015 and early March of 2024.

2.2 Preparing the Data

We will need to subset the 2023 data before we can impose it over a map. We will use the stringr library for this.

crime<-read.csv("https://pengdsci.github.io/STA553VIZ/w08/PhillyCrimeSince2015.csv")

df<-crime

df$year <- str_extract(df$date, "\\d{4}")
df$year <- as.numeric(df$year)

crime23<-subset(df, year==2023)

write.csv(crime23, "C:\\Users\\Alex\\Documents\\R\\Grad\\553\\datasets\\wk7.csv")

A copy of the 2023 data can be found at https://raw.githubusercontent.com/AlexDragonetti/STA553/main/hw7/wk7.csv

#remove observations with missing values - at least one has a missing value for coordinates
crime23.nona<-na.omit(crime23)

2.3 Mapping the Data

Finally, we will map the incident data using the leaflet package.

color2 <- rep("red", length(crime23.nona))
color2[which(crime23.nona$fatal=="Nonfatal")] <- "blue"
color2[which(crime23.nona$fatal=="Fatal")] <- "red"



label.msg <- paste("Street:", crime23.nona$street_name,    
                   "<br>Block Number:",crime23.nona$block_number,
                   "<br>Neighborhood:", crime23.nona$neighborhood,
                   "<br>Incident Type:", crime23.nona$fatal)


leaflet(crime23.nona) %>%
  addTiles() %>% 
  setView(lng=mean(crime23.nona$lng), lat=mean(crime23.nona$lat), zoom = 11) %>%
  addProviderTiles(providers$Esri.WorldGrayCanvas) %>%
  addCircleMarkers(
            ~lng, 
            ~lat,
            color = color2,
            stroke = FALSE, 
            fillOpacity = 0.5,
            popup= ~label.msg)  %>%
  addLegend(position = "bottomright", 
            colors = c("red", "blue"),
            labels= c("Fatal", "Nonfatal"),
            title= "Type of Incident",
            opacity = 0.4)

Our resulting graph is fully interactive - clicking a dot will show details of the incident.

Please note that the above graph has spots that appear purple. This is due to the opaque, overlapping red and blue dots, indicating both fatal and nonfatal incidents at the same address. For example, 1000 E Bristol Street in Juniata saw an incident with two victims, one being a fatality. For clarification on any confusing point, please refer to the dataset linked at the end of Preparing the Data.